Power and Thermal Management Systems and Methods for Autonomous Vehicles

ABSTRACT

Systems and methods for power and thermal management of autonomous vehicles are provided. In one example embodiment, a computing system includes processor(s) and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the processor(s) cause the computing system to perform operations. The operations include obtaining data associated with an autonomous vehicle. The operations include identifying one or more vehicle parameters associated with the autonomous vehicle based at least in part on the data associated with the autonomous vehicle. The operations include determining a modification to one or more operating characteristics of one or more systems onboard the autonomous vehicle based at least in part on the one or more vehicle parameters. The operations include controlling a heat generation of at least a portion of the autonomous vehicle via implementation of the modification of the operating characteristic(s) of the system(s) onboard the autonomous vehicle.

PRIORITY CLAIM

The present application is based on and claims priority to U.S.Provisional Application 62/555,895 having a filing date of Sep. 8, 2017,which is incorporated by reference herein.

FIELD

The present disclosure relates generally to controlling the powerconsumption and thermal management of an autonomous vehicle.

BACKGROUND

An autonomous vehicle is a vehicle that is capable of sensing itsenvironment and navigating with little to no human input. In particular,an autonomous vehicle can observe its surrounding environment using avariety of sensors and can attempt to comprehend the environment byperforming various processing techniques on data collected by thesensors. Given knowledge of its surrounding environment, the autonomousvehicle can navigate through such surrounding environment.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or may be learned fromthe description, or may be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to acomputer-implemented method of autonomous vehicle thermal management.The method includes identifying, by a computing system including one ormore computing devices, one or more vehicle parameters associated withan autonomous vehicle. The method includes determining, by the computingsystem, a modification to one or more operating characteristics of asensor system of the autonomous vehicle based at least in part on theone or more vehicle parameters. The method includes controlling, by thecomputing system, a heat generation of the sensor system of theautonomous vehicle via implementation of the modification of the one ormore operating characteristics of the sensor system.

Another example aspect of the present disclosure is directed to acomputing system for autonomous vehicle thermal management. Thecomputing system includes one or more processors and one or moretangible, non-transitory, computer readable media that collectivelystore instructions that when executed by the one or more processorscause the computing system to perform operations. The operations includeobtaining data associated with an autonomous vehicle. The data isindicative of at least one of a future action to be performed by theautonomous vehicle, a future geographic area in which the autonomousvehicle is to be located, or a weather condition to be experienced bythe autonomous vehicle. The operations include identifying one or morevehicle parameters associated with the autonomous vehicle based at leastin part on the data associated with the autonomous vehicle. Theoperations include determining a modification to one or more operatingcharacteristics of one or more systems onboard the autonomous vehiclebased at least in part on the one or more vehicle parameters. Theoperations include controlling a heat generation of at least a portionof the autonomous vehicle via implementation of the modification of theone or more operating characteristics of the one or more systems onboardthe autonomous vehicle.

Yet another example aspect of the present disclosure is directed to anautonomous vehicle. The autonomous vehicle includes one or moreprocessors and one or more tangible, non-transitory, computer readablemedia that collectively store instructions that when executed by the oneor more processors cause the autonomous vehicle to perform operations.The operations include obtaining data associated with the autonomousvehicle. The operations include identifying one or more vehicleparameters associated with an autonomous vehicle based at least in parton the data associated with the autonomous vehicle. The operationsinclude determining a modification to one or more operatingcharacteristics of one or more systems onboard the autonomous vehiclebased at least in part on the one or more vehicle parameters. The one ormore systems include at least one of a sensor system of the autonomousvehicle or a motion planning system of the autonomous vehicle. Theoperations include controlling a heat generation of at least a portionof the autonomous vehicle via implementation of the modification of theone or more operating characteristics of the one or more systems onboardthe autonomous vehicle.

Other example aspects of the present disclosure are directed to systems,methods, vehicles, apparatuses, tangible, non-transitorycomputer-readable media, and memory devices for predicting object motionand controlling autonomous vehicles with respect to the same.

These and other features, aspects and advantages of various embodimentswill become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the present disclosure and, together with thedescription, serve to explain the related principles.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art are set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts an example system overview according to exampleembodiments of the present disclosure;

FIG. 2 depicts an example implementation of a model according to exampleembodiments of the present disclosure;

FIGS. 3A and 3B depict example environments of an autonomous vehicleaccording to example embodiments of the present disclosure;

FIG. 4 depicts example geographic areas according to example embodimentsof the present disclosure;

FIG. 5 depicts a flow diagram of an example method of autonomous vehiclethermal management according to example embodiments of the presentdisclosure; and

FIG. 6 depicts example system components according to exampleembodiments of the present disclosure.

DETAILED DESCRIPTION

Reference now will be made in detail to embodiments, one or moreexample(s) of which are illustrated in the drawings. Each example isprovided by way of explanation of the embodiments, not limitation of thepresent disclosure. In fact, it will be apparent to those skilled in theart that various modifications and variations can be made to theembodiments without departing from the scope or spirit of the presentdisclosure. For instance, features illustrated or described as part ofone embodiment can be used with another embodiment to yield a stillfurther embodiment. Thus, it is intended that aspects of the presentdisclosure cover such modifications and variations.

Example aspects of the present disclosure are directed to selectivelyoperating one or more systems of an autonomous vehicle for improvedpower consumption and thermal management of the autonomous vehicle. Forinstance, an autonomous vehicle can include an onboard vehicle computingsystem that includes a variety of computing devices. As the computingdevices perform various tasks (e.g., data processing for autonomousoperation of the vehicle), the devices generate heat that can affect theoperation of the autonomous vehicle due to hardware heating. Forinstance, continuous operation of the vehicle's sensor system while theautonomous vehicle is slowing down and/or stopped can increase thetemperature of the autonomous vehicle due to the lack of airflow fromthe vehicle's motion. In accordance with the present disclosure, thevehicle's onboard computing system can help control the vehicle'stemperature by selectively operating certain systems onboard theautonomous vehicle (e.g., reducing the power consumption) and,ultimately, the heat generated by those systems. Moreover, the vehiclecomputing system can predictively reduce the power consumption/heatgeneration of these systems (e.g., while the vehicle is in motion) basedon parameters to occur at a future point in time. The vehicle computingsystem can identify one or more vehicle parameters associated with theautonomous vehicle to determine the circumstances faced/to be faced bythe vehicle, determine a modification to the system(s) onboard thevehicle, and implement the modification to control the vehicle'stemperature, heat generation, etc. By way of example, the vehiclecomputing system can evaluate the motion plan of the autonomous vehicleto determine that the vehicle will be stopping at a stop sign in fivehundred feet. As the autonomous vehicle approaches the stop sign, thevehicle computing system can reduce the frequency with which thevehicle's sensor system acquires image data associated with thevehicle's surroundings. This can decrease the power dissipated andthereby the heat generated by the sensor system. Accordingly, thevehicle computing system can predictively manage the systems onboard theautonomous vehicle to control the vehicle's heat generation,temperature, etc. This can help reduce potential overheating of theautonomous vehicle while also avoiding the need for more complex coolingsystems.

More particularly, an autonomous vehicle can be a ground-basedautonomous vehicle (e.g., car, truck, bus, etc.), aerial vehicle, oranother type of vehicle that can operate with minimal and/or nointeraction from a human operator located within the vehicle or at aremote location. The autonomous vehicle can include a vehicle computingsystem located onboard the autonomous vehicle to help control theautonomous vehicle. The vehicle computing system is located onboard theautonomous vehicle, in that the vehicle computing system is located onor within the autonomous vehicle. The vehicle computing system caninclude a sensor system, an autonomy system (e.g., for determining andplanning autonomous vehicle navigation/motion), one or more vehiclecontrol systems (e.g., for controlling braking, steering, powertrain),etc.

The sensor system can include one or more sensors that are configured toacquire sensor data associated with surrounding environment of theautonomous vehicle (e.g., within a field of view of the sensor(s)). Thesensor(s) can include, for example, cameras, a Light Detection andRanging (LIDAR) system, a Radio Detection and Ranging (RADAR) system,and/or other types of sensors. The sensor data can include image data,LIDAR data, RADAR data, etc.

The sensor data can be processed to help the autonomous vehicle perceiveits surrounding environment and generate an appropriate motion planthrough such surrounding environment. For example, the vehicle computingsystem (e.g., the autonomy system) can include a perception system, aprediction system, and a motion planning system. The perception systemcan be configured to perceive object(s) (e.g., vehicles, pedestrians,bicyclists, etc.) within the surrounding environment of the autonomousvehicle based at least in part on the sensor data. The prediction systemcan be configured to predict a motion of the perceived object(s). Themotion planning system can be configured to plan the motion of theautonomous vehicle, for example, with respect to the object(s) and thepredicted motion of the objects. The motion plan can be indicative ofone or more future vehicle action(s) (e.g., acceleration adjustments,speed adjustments, steering adjustments, etc.) to be performed by theautonomous vehicle. The vehicle computing system can utilize the vehiclecontrol system(s) to implement the determined motion plan.

The vehicle computing system can help control the temperature of theautonomous vehicle by selectively operating one or more of the systemsonboard the autonomous vehicle. To do so, the vehicle computing systemcan determine whether the appropriate circumstances existence in orderfor the autonomous vehicle to modify the operating characteristic(s) ofits onboard system(s). If so, the vehicle computing system can helpcontrol the temperature of the autonomous vehicle by reducing and/orlimiting the power consumption of one or more of these systems.

The vehicle computing system can identify one or more vehicle parametersassociated with the autonomous vehicle to determine whether it is safeand/or appropriate to modify the power consumption of the system(s)onboard the autonomous vehicle. For instance, the vehicle computingsystem can obtain data associated with the autonomous vehicle. Such datacan include data indicative of the vehicle's current operatingconditions. Additionally, or alternatively, such data can include dataindicative of a motion plan of the autonomous vehicle (e.g., thatindicates future vehicle actions). The data associated with theautonomous vehicle can also, or alternatively, include map data. The mapdata can be indicative of current and/or future geographic locations inwhich the autonomous vehicle is/will be located, information associatedwith a particular travel way, etc. Additionally, or alternatively, thedata associated with the autonomous vehicle can include weather data(e.g., indicative of current and/or future weather condition(s)), sensordata, and/or other types of data.

The vehicle computing system can identify the vehicle parameter(s)associated with the autonomous vehicle based at least in part on thedata associated with the autonomous vehicle. For instance, the vehiclecomputing system can process this data to determine whether certainvehicle parameters exist (or will exist) such that one or more systemsonboard the autonomous vehicle can be modified (e.g., to a reduced powerstate). For example, the vehicle computing system can process the dataindicative of the vehicle's current operating conditions to determine acurrent state (e.g., speed, heading, acceleration, etc.) of the vehicle.The vehicle computing system can process the data indicative of themotion plan of the autonomous vehicle to determine a future vehicleaction to be performed by the autonomous vehicle. The vehicle computingsystem can identify one or more vehicle parameters (e.g., such as futurevehicle speed, heading, acceleration, etc.) based at least in part onthe future vehicle action (e.g., a pull over action for a transportationservice, turn, etc.). In another example, the vehicle computing systemcan evaluate the map data to determine vehicle parameter(s) such as, forexample, current and/or future: travel way condition(s) (e.g.,obstructions, roadwork, etc.), travel way type (e.g., urban citystreets, rural highway, etc.), travel way geometry (e.g., slope,curvature, etc.), crowd density, traffic pattern(s), and/or otherparameters. Additionally, or alternatively, the vehicle computing systemcan process the weather data to determine the weather conditions thatthe vehicle is currently experiencing and/or will experience in thefuture. In some implementations, the vehicle computing system canprocess the sensor data to identify one or more vehicle parameters(e.g., road type, road conditions, weather conditions, etc.).

The vehicle computing system can determine a modification to one or moreoperating characteristics of the one or more systems onboard theautonomous vehicle based at least in part on the vehicle parameters. Insome implementations, the modification can aim to reduce the heatgeneration of the respective system by adjusting the operatingcharacteristics of that system. This can include an adjustment to howthe system's functions are operated (e.g., via a software modification),rather than a physical adjustment of the system hardware. In someimplementations, the vehicle computing system can include an algorithm(e.g., a rule(s)-based algorithm) that is stored in an accessible memoryonboard the autonomous vehicle. The vehicle parameter(s) can be providedas input into the algorithm. Based on the vehicle parameter(s), thealgorithm can indicate which of the system(s) onboard the autonomousvehicle should be modified (e.g., to reduce that system's heatgeneration) and how the respective system(s) should be modified (e.g.,which operating characteristics to modify). In some implementations, thesystem modification can be determined at least in part from a model,such as a machine-learned model. For example, the machine-learned modelcan be or can otherwise include one or more various model(s) such as,for example, models using boosted random forest techniques, neuralnetworks (e.g., deep neural networks), or other multi-layer non-linearmodels. Neural networks can include recurrent neural networks (e.g.,long short-term memory recurrent neural networks), feed-forward neuralnetworks, and/or other forms of neural networks. For instance,supervised training techniques can be performed to train the model(e.g., using previous logs of autonomous vehicle operations) todetermine which systems and types of modifications were most effectivein controlling the heat generation of a vehicle system, the temperatureof the vehicle, power consumption, etc. The input data for the model caninclude, for example, the vehicle parameters, as described herein. Themachine-learned model can provide, as an output, data indicative of oneor more recommended modifications to one or more operatingcharacteristics of one or more systems onboard the autonomous vehicle.

The vehicle computing system can determine system modifications forcurrent and/or future implementation on the autonomous vehicle. Themodification can be determined while the autonomous vehicle is in motion(e.g., rather than when parked). In some implementations, the vehiclecomputing system can determine a current modification for a systemonboard the autonomous vehicle based at least in part on the currentoperating conditions of the vehicle. By way of example, the vehiclecomputing system can determine that the vehicle's current speed isdecreasing and/or below a threshold and that the vehicle is travellingin a geographic area with a low crowd density (e.g., in a ruralenvironment without pedestrians). Given these vehicle parameter(s), thevehicle computing system can determine that the vehicle can safelyoperate below the maximum capability of the sensor system. As such, thevehicle computing system can determine a modification to the operatingcharacteristic(s) of the sensor system. The operating characteristic(s)associated with sensor system can include, for example, data acquisitioncharacteristics (e.g., associated with the collection of sensor data bya vehicle sensor) and/or data processing characteristics (e.g.,associated with the processing of such sensor data). For example, thevehicle computing system can determine (e.g., using the rule(s)-basedalgorithm, machine-learned model, etc.) that one or more of thevehicle's sensors (e.g., cameras) should be modified to utilize a lowerframe rate and/or acquire images a lower rate. This can allow the sensorsystem to consume less power for image acquisition/processing and,ultimately, to generate less heat. In another example, the vehiclecomputing system can determine that the vehicle is currently travellingon a relatively flat travel way (e.g., road). The vehicle computingsystem can determine a modification to the vehicle's sensor system tocause a decrease in the power consumption of the sensor system while thevehicle is on the flat travel way. For instance, the vehicle computingsystem can determine that the vehicle's sensors can utilize a smallerwindow of interest (e.g., +/−two degrees) for the flat travel way, whilestill maintaining safe operation of the vehicle. This can allow thesensor system to process less image data and, thus, decrease the powerconsumed for such processing, while also generating less heat.

In some implementations, the vehicle computing system can predictivelyreduce the power consumption of a system of the autonomous vehicle basedon future conditions that are expected by the vehicle. By way ofexample, the vehicle computing system can determine that the autonomousvehicle will decelerate to a stopped position at a future point in timebased at least in part on the vehicle's motion plan (e.g., whichindicates the future stopping action). The vehicle computing system canpreemptively determine that as the vehicle decelerates and/or reachesthe stopped position the operating characteristic(s) of the sensorsystem of the autonomous vehicle can be modified to control the system'spower consumption and heat generation. For example, the vehiclecomputing system can determine that once the vehicle eventually reachesa certain location, point in time, speed, etc. the sensor system cansub-sample the sensor data to decrease the power needed for imageprocessing. In some implementations, the vehicle computing system candetermine that one or more of the vehicle's sensors can be disabled(e.g., turned-off and/or otherwise prevented from acquiring sensordata). Either approach can lead to less heat generation from the sensorsystem.

In another example, the vehicle computing system can determine that theautonomous vehicle will be located within a future geographic area(e.g., a rural country road) that has a low level of traffic and a lowcrowd density (e.g., based on the map data, vehicle route, motion plan,etc.). Accordingly, the vehicle computing system can determine amodification to the operating characteristic(s) of the sensor system ofthe autonomous vehicle to be implemented when the vehicle is within thefuture geographic area, so that the sensor system consumes less power(and generates less heat). Such a modification can include, for example,reducing the resolution of the sensor data acquired via the sensor(s),adjusting the frame rate of the sensor, sub-sampling sensor data, etc.Additionally, or alternatively, the vehicle computing system candetermine that the autonomous vehicle will be located within a futuregeographic area (e.g., a city center) that has a higher crowd density(e.g., based on the map data, motion plan, etc.). Accordingly, thevehicle computing system can determine that the sensor system should notbe modified while in the future geographic area so that the vehicle canoperate to the maximum capability of the vehicle's sensors.

In some implementations, the vehicle computing system can identify acurrent and/or future weather condition to be experienced by theautonomous vehicle. In the event that the weather condition requiresoptimal sensor capability (e.g., wet/icy conditions, etc.), the vehiclecomputing system can determine that the operating characteristic(s) ofthe sensor system should not be modified to reduce power consumption,heat generation, etc. under such weather conditions. In the event thatthe weather condition does not require maximum sensor capability (e.g.,clear skies) and that it would be otherwise safe to do so, the vehiclecomputing system can adjust the operating characteristic(s) of thesensor system of the autonomous vehicle to decrease heat generation,power consumption, etc. (e.g., via acquisition frequency reduction,sub-sampling, etc.) while under such weather conditions. This can allowthe vehicle computing system to decrease the temperature of the sensorsystem under such circumstances.

The vehicle computing system can determine power-saving modifications tosystems onboard the autonomous vehicle other than the sensor system. Forinstance, the vehicle computing system can determine that thetemperature of the vehicle's surrounding environment is ninety degreesFahrenheit (e.g., based on vehicle thermometer, weather data, etc.). Insuch conditions, the vehicle computing system may reduce the frame-rateand/or implement sub-sampling by the sensor system. Additionally, oralternatively, the vehicle computing system can determine a modificationto one or more operating characteristics of the vehicle's motionplanning system. The operating characteristic(s) of the motion planningsystem can be characteristic(s) associated with the determination of thefuture action(s) of the autonomous vehicle. For example, a modificationof the operating characteristic(s) can include restricting the top speedof the autonomous vehicle while the vehicle is operating at the reducedframe rate, sub-sampling, etc. Such a modification can include anadjustment of the cost data utilized by the motion planning system whenplanning the motion of the autonomous vehicle. For instance, the costdata can be adjusted such that the cost of exceeding the restrictedspeed is very high. In another example, the autonomous vehicle may onlyhave enough thermal headway to operate the sensor(s) with a forwardoverhead of a particular distance (e.g., fifty meters). Accordingly, thevehicle computing system can determine a modification to the operatingcharacteristic(s) of the motion planning system to restrict the speed ofthe autonomous vehicle to not exceed a speed corresponding to thestopping distance at such forward overhead (e.g., twenty-five mph). Inanother example, the autonomous vehicle may be stopped for an extendedperiod of time due to emergency road construction on a highway. When theautonomous vehicle finally passes the roadwork, significant heat may begenerated if the vehicle attempts to reach the appropriate speed in ashort time period (e.g., due to sensor frame rate increase, additionalsensor activation, etc.). The vehicle computing system can modify theoperating characteristic(s) of the motion planning system such that theautonomous vehicle is caused to travel within a bufferspeed/acceleration as the vehicle is getting up to speed on the highway.This can help control the power consumption of the other vehicle systemsas the autonomous vehicle reaches the appropriate speed and, thus, theheat generation.

The modifications to the operating characteristic(s) of a system can beconfigured to adjust the system in a variety of manners. In someimplementations, the operating characteristic(s) of the system(s)onboard the autonomous vehicle can be modified in a proportional manner.By way of example, in the event that the speed of the autonomous vehicleis decreasing (or will be decreasing), the frame rate of a sensor can bedecreased proportionally to the decrease in vehicle speed. In someimplementations, the operating characteristic(s) of the system(s)onboard the autonomous vehicle can be modified in a pre-set manner. Forexample, in the event that the autonomous vehicle is travelling (or willbe travelling) below a certain speed threshold, in a certain weathercondition, and/or under another circumstance for which its existence canbe represented in a binary manner, the sensor system can begin tosub-sample the sensor data, according to a pre-determined approach thatdefines the particular sub-sampling type, factors, resolutions, etc.

The vehicle computing system can control the temperature of at least aportion of the autonomous vehicle via implementation of the modificationof the one or more operating characteristics of the one or more systemsonboard the autonomous vehicle. For instance, the vehicle computingsystem can control the heat generation of at least a portion of theautonomous vehicle via implementation of the modification of theoperating characteristic(s) of the system(s) onboard the autonomousvehicle. For instance, the vehicle computing system can send dataindicative of the determined modification (e.g., control signal(s)) tothe respective system(s) onboard the autonomous vehicle (e.g., sensorsystem, motion planning system) to implement the modification (e.g.,decrease in the frame rate, adjust a window of interest, adjust costdata, etc.). Such data can be provided while the autonomous vehicle isin motion. Moreover, such data can be sent at a current point in time toimmediately implement the modification (e.g., for the vehicle's currentsituation) or a future point in time to implement the modification forfuture conditions to be experienced by the vehicle (e.g., at a futurestop, flat travel way, low crowd density area, etc.). In someimplementations, the system(s) of the autonomous vehicle can beconfigured to operate in a plurality of operating modes that include,for example, a low heat operating mode. By way of example, the sensorsystem can be configured to operate in a plurality of sensor operatingmodes that include a low heat sensor operating mode in which thesensor(s) operate at a decreased frame rate, utilize sub-sampling, etc.To implement the determined modification, the vehicle computing systemcan cause the sensor system to enter into the low heat sensor operatingmode.

The systems and methods described herein provide a number of technicaleffects and benefits. For instance, the present disclosure provides animproved approach to thermal management of an autonomous vehicle. Inparticular, the systems and methods enable an autonomous vehicle toreduce the power consumption of the systems that are utilized by theautonomous vehicle for perceiving the surrounding environment (e.g.,sensor system) and planning its motion through the surroundingenvironment (e.g., autonomy system, motion planning system). By doingso, the autonomous vehicle can leverage its existing onboard systems tobetter control the vehicle's temperature (e.g., heat generation), ratherthan relying on more complex (and expensive) cooling systems. This canalso help avoid the difficulty associated with up-fitting such coolinghardware to an autonomous vehicle as well as the allocation ofcomputational resources needed to run such systems. Moreover, theimproved thermal management of the vehicle's onboard system can helpavoid potential overheating and, thus, lead to more efficient operationof the vehicle computing system.

The systems and methods of the present disclosure also provide animprovement to vehicle computing technology, such as autonomous vehiclecomputing technology. The systems and methods of the present disclosurecan control the heat generation of an autonomous vehicle by selectivelymodifying its onboard systems (e.g., the associated power consumption)when it is most appropriate. For instance, the vehicle computing systemcan obtain data associated with the autonomous vehicle. The data can beindicative of current and/or future operating conditions of theautonomous vehicle. For instance, the data can be indicative of acurrent or future action to be performed by the autonomous vehicle, acurrent or future geographic area in which the vehicle is to be located,a weather condition experienced (or to be experienced) by the autonomousvehicle, etc. The vehicle computing system can identify one or morevehicle parameters associated with an autonomous vehicle based at leastin part on the data associated with the autonomous vehicle. As describedherein, the vehicle parameter(s) can include, for example, currentand/or future: vehicle speed(s), vehicle action(s), travel waycondition(s) (e.g., obstructions, roadwork, etc.), travel way type(s)(e.g., urban city streets, rural highway, etc.), travel way geometry(e.g., slope, curvature, etc.), crowd density, traffic pattern(s),weather conditions, etc. The vehicle computing system can determine amodification to one or more operating characteristics of one or moresystems onboard the autonomous vehicle based at least in part on thevehicle parameters. The system(s) can be those associated with theautonomous operation of the vehicle such as, for example, the sensorsystem, motion planning system, etc. In some implementations,implementing the modification can cause a decrease in a powerconsumption of that respective system. The vehicle computing system canthereby control a temperature (e.g., heat generation) of at least aportion of the autonomous vehicle via implementation of themodification. In this way, the vehicle computing system can evaluate theappropriate vehicle parameters to ensure that the autonomous vehicle cansafely modify the vehicle's onboard autonomy operations to controlvehicle system heat generation when appropriate.

Additionally, the systems and methods of the present disclosure can beimplemented while the autonomous vehicle is in motion to scale downpower usage while the vehicle is in motion (e.g., rather than parked).This can be particularly helpful for autonomous vehicles that areequipped for both high-speed and low-speed operation. For example, whenan autonomous vehicle is operating (e.g., driving) at low-speed,high-speed sensors can be disengaged and/or their data collection may bethrottled for power consumption.

Moreover, the vehicle computing system can leverage its knowledge of thevehicle's future actions/operating conditions (e.g., via motion planningdata) to predictively determine system modifications that will beadvantageous for thermal management at a future point in time. This canenable the autonomous vehicle to preemptively control the temperature(e.g., heat generation) of the vehicle computing system, improving theability to avoid potential thermal harm to its computing components(e.g., from overheating). As described herein, the improved thermalmanagement can allow for more efficient operation of the vehicle'scomputing system (e.g., improved processing), under circumstances thatstill allow for the safe operation of the autonomous vehicle.

With reference now to the FIGS., example embodiments of the presentdisclosure will be discussed in further detail. FIG. 1 depicts anexample system 100 according to example embodiments of the presentdisclosure. The system 100 can include a vehicle computing system 102associated with a vehicle 104 and an operations computing system 106that is remote from the vehicle 104.

In some implementations, the vehicle 104 can be associated with anentity (e.g., a service provider, owner, manager). The entity can be onethat provides one or more vehicle service(s) to a plurality of users viaa fleet of vehicles that includes, for example, the vehicle 104. In someimplementations, the entity can be associated with only vehicle 104(e.g., a sole owner, manager). In some implementations, the operationscomputing system 106 can be associated with the entity. The vehicle 104can be configured to provide one or more vehicle services to one or moreusers 108 (e.g., services offered by the entity). The vehicle service(s)can include transportation services (e.g., rideshare services in which auser 108 rides in the vehicle 104 to be transported), courier services,delivery services, and/or other types of services. The vehicleservice(s) can be offered to users 108 by the entity, for example, via asoftware application (e.g., a mobile phone software application). Insome implementations, the entity can utilize the operations computingsystem 106 to coordinate and/or manage the vehicle 104 (and itsassociated fleet, if any) to provide the vehicle services to a user 108.

The operations computing system 106 can include one or more computingdevices that are remote from the vehicle 104 (e.g., located off-boardthe vehicle 104). For example, such computing device(s) can becomponents of a cloud-based server system and/or other type of computingsystem that can communicate with the vehicle computing system 102 of thevehicle 104. The computing device(s) of the operations computing system106 can include various components for performing various operations andfunctions. For instance, the computing device(s) can include one or moreprocessor(s) and one or more tangible, non-transitory, computer readablemedia (e.g., memory devices). The one or more tangible, non-transitory,computer readable media can store instructions that when executed by theone or more processor(s) cause the operations computing system 106(e.g., the one or more processors, etc.) to perform operations andfunctions, such as coordinating vehicles to provide vehicle services.

The vehicle 104 can include a communications system 110 configured toallow the vehicle computing system 102 (and its computing device(s)) tocommunicate with other computing devices. The vehicle computing system102 can use the communications system 110 to communicate with theoperations computing system 106 and/or one or more other remotecomputing device(s) over one or more networks (e.g., via one or morewireless signal connections). In some implementations, thecommunications system 110 can allow communication among one or more ofthe system(s) on-board the vehicle 104. The communications system 110can also be configured to enable the autonomous vehicle to communicateand/or otherwise receive data from other computing devices (e.g., a userdevice). The communications system 110 can include any suitablecomponents for interfacing with one or more network(s), including, forexample, transmitters, receivers, ports, controllers, antennas, and/orother suitable components that can help facilitate communication.

The vehicle 104 incorporating the vehicle computing system 102 can be aground-based autonomous vehicle (e.g., car, truck, bus, etc.), anair-based autonomous vehicle (e.g., airplane, drone, helicopter, orother aircraft), or other types of vehicles (e.g., watercraft, etc.).The vehicle 104 can be an autonomous vehicle that can drive, navigate,operate, etc. with minimal and/or no interaction from a human operator.In some implementations, a human operator can be omitted from thevehicle 104 (and/or also omitted from remote control of the vehicle104).

The vehicle 104 can be configured to operate in a plurality of vehicleoperating modes. The vehicle 104 can be configured to operate in a fullyautonomous (e.g., self-driving) operating mode in which the vehicle 104can drive and navigate with no input from a user 108 present in thevehicle 104 (and/or at a remote location). The vehicle 104 can beconfigured to operate in a semi-autonomous operating mode in which thevehicle 104 can operate with some input from a user 108 present in thevehicle 104 (and/or at a remote location). The vehicle 104 can enterinto a manual operating mode in which the vehicle 104 is fullycontrollable by a user 108 (e.g., human operator) and can be prohibitedfrom performing autonomous navigation (e.g., autonomous driving). Insome implementations, the vehicle 104 can implement vehicle operatingassistance technology (e.g., collision mitigation system, power assiststeering, etc.) while in the manual operating mode to help assist theoperator of the vehicle 104. The power and thermal management systemsand methods described herein can be implemented while the vehicle 104 isin any one of the plurality of vehicle operating modes.

The operating mode of the vehicle 104 can be adjusted in a variety ofmanners. In some implementations, the operating mode of the vehicle 104can be selected remotely, off-board the vehicle 104. For example, anentity associated with the vehicle 104 (e.g., a service provider) canutilize the operations computing system 106 to manage the vehicle 104(and/or an associated fleet). The operations computing system 106 cansend one or more control signals to the vehicle 104 instructing thevehicle 104 to enter into, exit from, maintain, etc. an operating mode.By way of example, the operations computing system 106 can send one ormore control signals to the vehicle 104 instructing the vehicle 104 toenter into the fully autonomous operating mode. In some implementations,the operating mode of the vehicle 104 can be set onboard and/or near thevehicle 104. For example, the vehicle computing system 102 canautomatically determine when and where the vehicle 104 is to enter,change, maintain, etc. a particular operating mode (e.g., without userinput). Additionally, or alternatively, the operating mode of thevehicle 104 can be manually selected via one or more interfaces locatedonboard the vehicle 104 (e.g., key switch, button, etc.) and/orassociated with a computing device proximate to the vehicle 104 (e.g., atablet operated by authorized personnel located near the vehicle 104).In some implementations, the operating mode of the vehicle 104 can beadjusted based at least in part on a sequence of interfaces located onthe vehicle 104. For example, the operating mode may be adjusted bymanipulating a series of interfaces in a particular order to cause thevehicle 104 to enter into a particular operating mode.

The vehicle computing system 102 can include one or more computingdevices located onboard the vehicle 104. For example, the computingdevice(s) can be located on and/or within the vehicle 104. The computingdevice(s) can include various components for performing variousoperations and functions. For instance, the computing device(s) caninclude one or more processor(s) and one or more tangible,non-transitory, computer readable media (e.g., memory devices). The oneor more tangible, non-transitory, computer readable media can storeinstructions that when executed by the one or more processor(s) causethe vehicle 104 (e.g., its computing system, one or more processors,etc.) to perform operations and functions, such as those describedherein for power consumption and thermal management.

As shown in FIG. 1, the vehicle 104 can include a sensor system 111 thatincludes one or more sensors 112, an autonomy computing system 114, andone or more vehicle control systems 116. One or more of these systemscan be configured to communicate with one another via a communicationchannel. The communication channel can include one or more data buses(e.g., controller area network (CAN)), on-board diagnostics connector(e.g., OBD-II), and/or a combination of wired and/or wirelesscommunication links. The onboard systems can send and/or receive data,messages, signals, etc. amongst one another via the communicationchannel.

The sensor(s) 112 can be configured to acquire sensor data 118associated with one or more objects that are proximate to the vehicle104 (e.g., within a field of view of one or more of the sensor(s) 112).The sensor(s) 112 can include a LIDAR system, a RADAR system, one ormore cameras (e.g., visible spectrum cameras, infrared cameras, etc.),motion sensors, and/or other types of imaging capture devices and/orsensors. The sensor data 118 can include image data, radar data, LIDARdata, and/or other data acquired by the sensor(s) 112. The object(s) caninclude, for example, vehicles, pedestrians, bicycles, and/or otherobjects. The object(s) can be located in front of, to the rear of,above, below, and/or to the side of the vehicle 104. The sensor data 118can be indicative of locations associated with the object(s) within thesurrounding environment of the vehicle 104 at one or more times. Thesensor(s) 112 can provide the sensor data 118 to the autonomy computingsystem 114.

In addition to the sensor data 118, the vehicle computing system 102 canretrieve or otherwise obtain other types of data associated withgeographic area(s) in which the objects (and/or the vehicle 104) havebeen, are, and/or will be located. For example, the vehicle computingsystem 102 can obtain map data 120 that provides detailed informationabout the surrounding environment of the vehicle 104. The map data 120can be indicative of current and/or future geographic locations in whichthe autonomous vehicle is/will be located, information associated with aparticular travel way, etc. The map data 120 can provide informationregarding: the identity and location of different roadways, roadsegments, buildings, sidewalks, walls, or other items; the location anddirections of traffic lanes (e.g., the boundaries, location, direction,etc. of a parking lane, a turning lane, a bicycle lane, or other laneswithin a particular travel way); traffic control data (e.g., thelocation and instructions of signage, traffic lights, or other trafficcontrol devices); the location of obstructions (e.g., roadwork,accident, etc.) and/or any other map data that provides information thatassists the computing system in comprehending and perceiving itssurrounding environment and its relationship thereto. In someimplementations, the vehicle computing system 102 can obtain satelliteimagery of a geographic area (e.g., overhead imagery) in which theobject(s) and/or the vehicle 104 is located. Such satellite imagery canbe provided to the vehicle computing system 102 from the operationscomputing system 106 and/or other computing device(s) that are remotefrom the vehicle 104.

Additionally, or alternatively, the vehicle computing system 102 canobtain weather data 121. The weather data 121 can be indicative of oneor more past, current, and/or future weather conditions. The weathercondition(s) can be associated with geographic area(s) in which thevehicle 104 is travelling and/or will be travelling. For example, theweather data 121 can be indicative of whether a particular geographicarea is to experience wet, icy, snowy, dry, humid, sunny, hot, cold,and/or other weather conditions. In some implementations, the weatherdata 121 can be indicative of one or more times at which these weathercondition(s) are expected to occur.

The vehicle 104 can include a positioning system 122. The positioningsystem 122 can determine a current position of the vehicle 104. Thepositioning system 122 can be any device or circuitry for analyzing theposition of the vehicle 104. For example, the positioning system 122 candetermine position by using one or more of inertial sensors, a satellitepositioning system, based on IP/MAC address, by using triangulationand/or proximity to network access points or other network components(e.g., cellular towers, WiFi access points, etc.) and/or other suitabletechniques. The position of the vehicle 104 can be used by varioussystems of the vehicle computing system 102 and/or provided to one ormore remote computing device(s) (e.g., of the operations computingsystem 106). For example, the map data 120 can provide the vehicle 104relative positions of the surrounding environment of the vehicle 104.The vehicle 104 can identify its position within the surroundingenvironment (e.g., across six axes) based at least in part on the datadescribed herein. For example, the vehicle 104 can process the sensordata 118 (e.g., LIDAR data, camera data) to match it to a map of thesurrounding environment to get an understanding of the vehicle'sposition within that environment.

The autonomy computing system 114 can include a perception system 124, aprediction system 126, a motion planning system 128, and/or othersystems that cooperate to perceive the surrounding environment of thevehicle 104 and determine a motion plan for controlling the motion ofthe vehicle 104 accordingly. For example, the autonomy computing system114 can receive the sensor data 118 from the sensor(s) 112, attempt tocomprehend the surrounding environment by performing various processingtechniques on the sensor data 118 (and/or other data), and generate anappropriate motion plan through such surrounding environment. Theautonomy computing system 114 can control the one or more vehiclecontrol systems 116 to operate the vehicle 104 according to the motionplan.

The autonomy computing system 114 can identify one or more objects thatare proximate to the vehicle 104 based at least in part on the sensordata 118 and/or the map data 120. For example, the perception system 124can process the sensor data 118 to detect one or more objects that arewithin the surrounding environment of the vehicle 104 as well as statedata 130 associated therewith. The state data 130 can be indicative ofat least a current or past state of an object that is within thesurrounding environment of the vehicle 104. For example, the state data130 for each object can describe an estimate of the object's currentand/or past location (also referred to as position), current and/or pastspeed/velocity, current and/or past acceleration, current and/or pastheading, current and/or past orientation, size/footprint, class (e.g.,vehicle class vs. pedestrian class vs. bicycle class), the uncertaintiesassociated therewith, and/or other state information. The perceptionsystem 124 can provide the state data 130 to the prediction system 126.

The prediction system 126 can create predicted data 132 associated witheach of the respective one or more objects proximate to the vehicle 104.The predicted data 132 can be indicative of one or more predicted futurelocations of each respective object that are determined as furtherdescribed herein. The predicted data 132 can be indicative of apredicted trajectory (e.g., predicted path) of at least one objectwithin the surrounding environment of the vehicle 104. For example, thepredicted trajectory can indicate a path along which the respectiveobject is predicted to travel over time. In some implementations, thepredicted data 132 can indicate the speed at which the object ispredicted to travel along the predicted trajectory. The predictionsystem 126 can provide the predicted data 132 associated with theobject(s) to the motion planning system 128, for generation of a motionplan 134.

The motion planning system 128 can determine a motion plan 134 for thevehicle 104 based at least in part on the predicted data 132 (and/orother data). The motion plan 134 can include vehicle actions (e.g.,future vehicle actions) with respect to the objects proximate to thevehicle 104 as well as the predicted movements. For instance, the motionplanning system 128 can implement an optimization algorithm thatconsiders cost data associated with a vehicle action as well as otherobjective functions (e.g., cost functions based on speed limits, trafficlights, etc.), if any, to determine optimized variables that make up themotion plan 134. By way of example, the motion planning system 128 candetermine that the vehicle 104 can perform a certain action (e.g., passan object) without increasing the potential risk to the vehicle 104and/or violating any traffic laws (e.g., speed limits, lane boundaries,signage). The motion plan 134 can include a planned trajectory, speed,acceleration, other actions, etc. of the vehicle 104.

The motion planning system 128 can provide the motion plan 134 with dataindicative of the vehicle actions, a planned trajectory, and/or otheroperating parameters to the vehicle control system(s) 116 to implementthe motion plan 134 for the vehicle 104. For instance, the vehicle 104can include a mobility controller configured to translate the motionplan 134 into instructions. By way of example, the mobility controllercan translate a determined motion plan 134 into instructions to adjustthe steering of the vehicle 104 “X” degrees, apply a certain magnitudeof braking force, etc. The mobility controller can send one or morecontrol signals to the responsible vehicle control component (e.g.,braking control system, steering control system, acceleration controlsystem) to execute the instructions and implement the motion plan 134.

The vehicle computing system 102 can be configured to can help controlthe temperature of the vehicle 104 by selectively operating one or moreof the systems onboard the vehicle 104. For example, the vehiclecomputing system 102 can be configured to control the heat generation ofa system onboard the vehicle 104. To do so, the vehicle computing system102 can determine whether the appropriate circumstances existence inorder for the vehicle computing system 102 to modify the operatingcharacteristic(s) of one or more of the onboard system(s). The operatingcharacteristic(s) can be indicative of the parameters by which arespective system performs its respective functions. The vehiclecomputing system 102 can help control the heat generation of the vehicle104 by reducing and/or limiting the power consumption of one or more ofthese systems via modification of the system's operatingcharacteristic(s), while the vehicle 104 is in motion. In someimplementations, the vehicle 104 may increase power consumption (e.g.,to the extent supplemental cooling measures are needed by an onboardcooling system).

The vehicle computing system 102 can be configured to identify one ormore vehicle parameters 140 associated with the vehicle 104 to determinewhether it is safe and/or appropriate to modify the heat generation,power consumption, etc. of the system(s) onboard the vehicle 104. Forinstance, the vehicle computing system 102 can obtain data associatedwith the vehicle 104. In some implementations, the data associated withthe vehicle 104 can include data indicative of the vehicle's currentoperating conditions such as, for example, a current heading, speed,acceleration, temperature of onboard vehicle components (e.g., sensors,processors, other computing components, etc., and/or other conditions.

In some implementations, the data associated with the vehicle 104 can beindicative of at least one of a future action to be performed by thevehicle 104, a future geographic area in which the vehicle 104 is to belocated, or a weather condition to be experienced by the vehicle 104.For example, the data associated with the vehicle 104 can include dataindicative of a motion plan 134 of the vehicle 104 (e.g., that indicatesone or more future vehicle actions to be performed by the vehicle 104).Additionally, or alternatively, the data associated with the vehicle 104can include the map data 120. As described herein, the map data 120 canbe indicative of current and/or future geographic area in which thevehicle 104 is/is to be located, information associated with aparticular travel way, etc. The data associated with the vehicle 104 caninclude the weather data 121. The weather data can be indicative ofcurrent and/or future weather condition(s) that may be experienced bythe vehicle 104 (and/or a particular geographic area). In someimplementations, the data associated with the vehicle 104 can includethe sensor data 118. The sensor data 118 can be indicative of thesurrounding environment of the vehicle 104 and the objects locatedtherein. The data associated with the vehicle 104 can include othertypes of data.

The vehicle computing system 102 can be configured to identify the oneor more vehicle parameters 140 associated with the vehicle 104 based atleast in part on the data associated with the vehicle 104. For instance,the vehicle computing system 102 can process this data to determinewhether certain vehicle parameters exist (or will exist) such that oneor more systems onboard the vehicle 104 can be modified (e.g., to areduced power state, to a reduced heat generation state, etc.). Forexample, the vehicle computing system 102 can process the dataindicative of the vehicle's current operating conditions to determinevehicle parameter(s) 140 such as a current state (e.g., speed, heading,acceleration, etc.) of the vehicle 104, a temperature of vehiclecomponents/computing devices (e.g., sensors, processors, etc.), and/orother parameter(s). The vehicle computing system 102 can process thedata indicative of the motion plan 134 of the vehicle 104 to determinevehicle parameter(s) 140 such as a future vehicle action to be performedby the vehicle 104. The future vehicle action can include, for example,a future vehicle action that decreases the speed of the vehicle 104, afuture vehicle action that changes a heading of the vehicle 104 (e.g., aturn), a future stopping action, a future pull over action to provide avehicle service (e.g., transportation service, a courier service, adelivery service, etc.), and/or other future vehicle actions. Thevehicle computing system 102 can identify a future vehicle speed,heading, acceleration, location, etc. based at least in part on thefuture vehicle action (e.g., a pull over action for a transportationservice, turn, etc.). In another example, the vehicle computing system102 can process the map data 120 to determine vehicle parameter(s) 140such as, for example, current and/or future: travel way condition(s)(e.g., obstructions, roadwork, etc.), travel way type (e.g., urban citystreets, rural highway, etc.), travel way geometry (e.g., slope,curvature, etc.), crowd density, traffic pattern(s), and/or othervehicle parameters indicated by the map data 120. Additionally, oralternatively, the vehicle computing system 102 can process the weatherdata 121 to determine vehicle parameter(s) 140 such as the weatherconditions that the vehicle 104 is currently experiencing and/or willexperience as a future point in time. In some implementations, thevehicle computing system 102 can process the sensor data 118 to identifyone or more vehicle parameters 140 (e.g., road type, road conditions,weather conditions, etc.) and/or other vehicle parameter(s).

The vehicle computing system 102 can be configured to determine amodification to one or more operating characteristics of the one or moresystems onboard the vehicle 104 based at least in part on the one ormore vehicle parameters 140. Such a modification can be determined whilethe vehicle 104 is in motion (e.g., travelling for a vehicle service).The modification can aim to reduce the heat generation of the respectivesystem by adjusting one or more of the operating characteristics of thatsystem. In some implementations, this can include an adjustment to howthe system's functions are operated (e.g., via a software modification),rather than a physical adjustment of the system hardware. In someimplementations, the physical operating characteristics (e.g., spinrate) of the respective system (e.g., LIDAR system) can be modified(e.g., in real-time, near real-time, etc.). The modification can, forexample, throttle data collection and/or data analysis. The modificationcan cause a decrease in power consumption by that system.

In some implementations, the vehicle computing system 102 can include analgorithm (e.g., a rule(s)-based algorithm) that is stored in anaccessible memory onboard the vehicle 104. The vehicle parameter(s) 140can be provided as input into the algorithm. The algorithm can indicatewhich of the system(s) onboard the vehicle 104 should be modified (e.g.,to reduce that system's power consumption) and how the respectivesystem(s) should be modified (e.g., which operating characteristics tomodify) based at least in part on the one or more vehicle parameter 140.

In some implementations, the modification to the one or more operatingcharacteristics of a system onboard the vehicle 104 can be determined atleast in part from a model, such as a machine-learned model. Forexample, FIG. 2 depicts an example implementation 200 of a model 202according to example embodiments of the present disclosure. The vehiclecomputing system can include, employ, and/or otherwise leverage themodel 202 to help determine a modification to one or more operatingcharacteristics of one or more systems onboard the vehicle 104. Inparticular, the model 202 can be a machine-learned model. For example,the machine-learned model can be or can otherwise include one or morevarious model(s) such as, for example, neural networks (e.g., deepneural networks), or other multi-layer non-linear models. Themachine-learned model can include neural networks such as, for example,a convolutional neural networks, recurrent neural networks (e.g., longshort-term memory recurrent neural networks), feed-forward neuralnetworks, and/or other forms of neural networks.

The model 202 can be trained to determine a modification to one or moreoperating characteristics of a system onboard the vehicle 104. Forinstance, training techniques (e.g., supervised training techniques) canbe performed to train the model 202 to determine which operatingcharacteristics of which systems to modify. By way of example, the model202 can be trained based on, for example, a number of sets of data fromprevious events (e.g., previous logs of vehicle operations). Thetraining data can be associated with a data recorded from previouslymodified systems and operating characteristics (e.g., which weremodified for power consumption/thermal management). The training datacan allow the model 202 to be trained to determine which systems andtypes of modifications were most effective in controlling thetemperature of the vehicle 104 (e.g., via the heat generated by thevehicle system(s)). The model can be trained based on training dataassociated with the vehicle 104 and/or other vehicle(s).

The model 202 can be configured to receive input data 204 and provideoutput data 206 based at least in part on the input data 204. Forexample, the input data 204 for the model 202 can include the vehicleparameters 140, as described herein. The vehicle computing system 102can obtain data indicative of a model 202 (e.g., machine-learned model)from an accessible memory located onboard the vehicle 104. The vehiclecomputing system 102 can provide input data 204 to the model 202. Themodel 202 can provide, as an output, output data 206 indicative of oneor more recommended modifications to one or more operatingcharacteristics of one or more systems onboard the vehicle 104.

The vehicle computing system 102 can determine a modification of theoperating characteristic(s) of an onboard system for current and/orfuture implementation on the vehicle 104. The modification can bedetermined while the vehicle 104 is in motion (e.g., rather than whenparked). The one or more systems onboard the vehicle 104 can include atleast one of the sensor system 111 of the vehicle 104 or the motionplanning system 128 of the vehicle 104. Additionally, or alternatively,the modification can be associated with another system onboard thevehicle 104.

In some implementations, the vehicle computing system 102 can determinea current modification for a system onboard the vehicle 104 based atleast in part on the current operating conditions of the vehicle 104. Byway of example, the vehicle computing system 102 can determine that thevehicle's current speed is decreasing and/or below a threshold and thatthe vehicle 104 is travelling in a geographic area with a low crowddensity (e.g., in a rural environment without pedestrians). The vehiclecomputing system 102 can determine that the vehicle 104 can safelyoperate below the maximum capability of the sensor system 111 based atleast in part on such vehicle parameter(s) 140. Accordingly, the vehiclecomputing system 102 can determine a modification to one or moreoperating characteristics of the sensor system 111. The operatingcharacteristic(s) of the sensor system 111 can include, for example,data acquisition characteristics (e.g., associated with the collectionof the sensor data 118 by a sensor 112) and/or data processingcharacteristics (e.g., associated with the processing of the sensor data118). For instance, the vehicle computing system 102 can determine(e.g., using the rule(s)-based algorithm, model 202, etc.) that one ormore of the sensors 118 (e.g., cameras) should be modified to utilize alower frame rate and/or acquire sensor data 118 a lower rate. This canallow the sensor system 111 to consume less power for imageacquisition/processing and, ultimately, to generate less heat.

In another example, the modification to the sensor system can include amodification to the physical operating characteristic(s) of the sensorsystem. For example, a sensor (e.g., spinning LIDAR sensor) can bemodified to reduce the spin rate of the sensor (e.g., from 10 Hz to 5Hz). Additionally, or alternatively, the modification can include areduction (e.g., by one half) in the firing rate of the sensor'semissions (e.g., lasers). This can lead to a reduction in the frequencyof emissions for the same resolution sensor frames. Moreover, this canlead to a significant impact on the permissible ambient temperature forsensor operation.

In another example, the vehicle computing system 102 can determine thatthe vehicle 104 is currently travelling on a relatively hilly travel way302, as shown for example in the example environment 300 of vehicle 104in FIG. 3A. Due to the type of travel way 302, the vehicle computingsystem 102 can determine that a window of interest 304 of the sensorsystem 111 should not be decreased such that the vehicle 104 canaccurately perceive the travel way 302 (and its change in elevation). Insuch a case, the vehicle computing system 102 would refrain frommodifying the window of interest of the sensor system 111 (e.g., toreduce power consumption, heat generation, etc. of the sensor system111). The vehicle 104 may eventually begin travelling on a relativelyflat travel way 322 of the example environment 320 of the vehicle 104 asshown in FIG. 3B. The vehicle computing system 102 can determine amodification to the operating characteristic(s) of the sensor system 111to cause a decrease in the heat generation of the sensor system 111while the vehicle 104 is on the flat travel way 322. For example, thevehicle computing system 102 can determine that the sensors 112 canutilize a smaller window of interest 304 (e.g., +/−two degrees) for theflat travel way 322, while still maintaining safe operation of thevehicle 104. Accordingly, the vehicle computing system 102 can determinea modification to the operating characteristic(s) of the sensor system111 that includes adjusting the window of interest 304 of a sensor 112of the sensor system 111. The reduced window of interest 304 can allowthe sensor system 111 to process less sensor data 118 (e.g., imagedata), decrease the power consumed for such processing and, thus,decrease heat generation.

In some implementations, the vehicle computing system 102 canpredictively reduce the power consumption of a system of the vehicle 104based on future conditions that are expected by the vehicle 104. FIG. 4depicts a diagram 400 of geographic areas according to exampleembodiments of the present disclosure. By way of example, the vehiclecomputing system 102 can determine, at a first point in time, that thevehicle 104 will decelerate to a stopped position 402 (e.g., at a stopsign) at a second, future point in time (e.g., occurring after the firstpoint in time) based at least in part on the motion plan 134 (e.g.,which indicates the future stopping action). The vehicle computingsystem 102 can preemptively determine that as the vehicle 104decelerates and/or reaches the stopped position the operatingcharacteristic(s) of the sensor system 111 of the vehicle 104 can bemodified to control the heat generated by the sensor system 111. Forexample, the vehicle computing system 102 can determine that once thevehicle 104 eventually reaches a certain location, point in time, speed,etc. the sensor system 111 can sub-sample the sensor data 118 todecrease the power needed for data processing. In some implementations,the vehicle computing system 102 can determine that one or more of thesensors 112 can be disabled (e.g., turned-off and/or otherwise preventedfrom acquiring sensor data 118). These modifications in operatingcharacteristic(s) of the sensor system 111 can lead to less heatgeneration from the sensor system 111.

In another example, the vehicle computing system 102 can determine thatthe vehicle 104 will be located within a future geographic area/location404 that has a low level of traffic and a low crowd density based atleast in part on the data associated with the vehicle 104 (e.g., the mapdata 120, motion plan 134, a vehicle route, etc.). Accordingly, thevehicle computing system 102 can determine a modification to theoperating characteristic(s) of the sensor system 111 of the vehicle 104to be implemented when the vehicle 104 is within the future geographicarea 404, so that the sensor system 111 consumes less power (andgenerates less heat). The modification of the one or more operatingcharacteristics of the sensor system 111 can include, for example, atleast one of adjusting an acquisition of sensor data 118 (e.g., imagedata) by a sensor 112, adjusting a window of interest 304 associatedwith the sensor 112, sub-sampling sensor data 118 acquired via a sensor112, reducing the resolution of the sensor data 118 acquired via asensor 112, reducing a motion rate (e.g., spin rate) of a sensor 112,reducing emission frequency (e.g., laser firing rate) of a sensor 112,or disabling a sensor 112. Additionally, or alternatively, the vehiclecomputing system 102 can determine that the vehicle 104 will be locatedwithin a future geographic area 406, 408 that has a higher level oftraffic and/or a higher crowd density (e.g., based at least in part onthe map data 120, motion plan 134, etc.). Accordingly, the vehiclecomputing system 102 can determine that the sensor system 111 should notbe modified while in the future geographic areas 406, 408 so that thevehicle 104 can operate to the maximum capability of the sensors 112. Inanother example, the vehicle computing system 102 can process the dataassociated with the vehicle 104 (e.g., motion plan 134, map data 120) todetermine that the vehicle 104 will travel within proximity of anobstruction 410 in a travel way. As such, the vehicle computing system102 can determine that the sensor system 111 should not be modified asthe vehicle 104 attempts to traverse the obstruction 410.

In some implementations, the sensor system can be modified to increasethe capability of the sensor system 111. For instance, in the event thatthe sensors 112 run at one quarter of a max frame rate, the vehiclecomputing system 102 can increase the frame rate (e.g., in a highdensity area). The vehicle computing system could compensate forincreased cooling (e.g., via the onboard cooling system) to help offsetany additional heat generation.

In some implementations, the vehicle computing system 102 can identify acurrent and/or future weather condition 412 to be experienced by thevehicle 104. In the event that the weather condition 412 requiresoptimal sensor capability (e.g., wet/icy conditions, etc.), the vehiclecomputing system 102 can determine that the operating characteristic(s)of the sensor system 111 should not be modified to reduce powerconsumption under such weather conditions. In the event that the weathercondition 412 does not require maximum sensor capability (e.g., clearskies) and that it would be otherwise safe to do so, the vehiclecomputing system 102 can determine a modification to the operatingcharacteristic(s) of the sensor system 111 of the vehicle 104 (e.g., viaacquisition frequency reduction, sub-sampling, etc.) while under suchweather conditions 412. This can allow the vehicle computing system 102to decrease the heat generation, temperature, etc. of the sensor system111 under such circumstances.

The vehicle computing system 102 can determine modifications to systemsonboard the vehicle 104 other than the sensor system 111. For instance,the vehicle computing system 102 can determine that the temperature ofthe vehicle's surrounding environment is ninety degrees Fahrenheit(e.g., based on a vehicle thermometer, weather data 121, etc.). In suchconditions, the vehicle computing system 102 may determine amodification that reduces the frame-rate and/or implements sub-samplingby the sensor system 111. Additionally, or alternatively, the vehiclecomputing system 102 can determine a modification to one or moreoperating characteristics of the motion planning system 128. Theoperating characteristic(s) of the motion planning system 128 can becharacteristic(s) associated with the determination of the vehicle'smotion (e.g., one future vehicle action(s)).

For example, a modification of the operating characteristic(s) caninclude restricting the top speed of the vehicle 104 while the vehicle104 is operating at the reduced frame rate, sub-sampling, etc. Themodification of the one or more operating characteristics can includeadjusting of the cost data utilized by the motion planning system 128when planning the motion of the vehicle 104. For instance, the cost datacan be adjusted such that the cost of exceeding the restricted speed isvery high. In another example, the vehicle 104 may only have enoughthermal headway to operate the sensor(s) 112 with a forward overhead ofa particular distance (e.g., fifty meters). Accordingly, the vehiclecomputing system 102 can determine a modification to the operatingcharacteristic(s) of the motion planning system 128 to restrict thespeed of the vehicle 104 to not exceed a speed corresponding to thestopping distance at such forward overhead (e.g., twenty-five mph).

In another example, the vehicle 104 may be stopped for an extendedperiod of time due to emergency road construction on a highway. When thevehicle 104 finally passes the roadwork, significant heat may begenerated if the vehicle 104 attempts to reach the appropriate speed ina short time period (e.g., due to sensor frame rate increase, additionalsensor activation, etc.). The vehicle computing system 102 can modifythe operating characteristic(s) of the motion planning system 128 suchthat the vehicle 104 is caused to travel within a bufferspeed/acceleration as the vehicle 104 is getting up to speed on thehighway. This can help control the power consumption/heat generation ofthe other vehicle systems as the vehicle 104 reaches the appropriatespeed.

The modifications to the operating characteristic(s) of a system can beconfigured to adjust the respective system in a variety of manners. Insome implementations, the operating characteristic(s) of the system(s)onboard the vehicle 104 can be modified in a proportional manner. By wayof example, in the event that the speed of the vehicle 104 is decreasing(or will be decreasing), the frame rate of a sensor 112 can be decreasedproportionally to the decrease in vehicle speed. In someimplementations, the operating characteristic(s) of the system(s)onboard the vehicle 104 can be modified in a pre-set manner. Forexample, in the event that the vehicle 104 is travelling (or will betravelling) below a certain speed threshold, in a certain weathercondition, and/or under another circumstance for which its existence canbe represented in a binary manner, the sensor system 111 can begin tosub-sample the sensor data 118, according to a pre-determined approachthat defines the particular sub-sampling type, factors, resolutions,etc.

The vehicle computing system can be configured to control the heatgeneration of at least a portion of the vehicle 104 (e.g., the modifiedsystem) via implementation of the modification of the one or moreoperating characteristics of the one or more systems onboard the vehicle104. For instance, the vehicle computing system 102 can send dataindicative of the determined modification (e.g., control signal(s)) tothe respective system(s) onboard the vehicle 104 (e.g., sensor system111, motion planning system 128, etc.) to implement the modification(e.g., decrease in the frame rate, adjust a window of interest, adjustcost data, etc.). The vehicle computing system 102 can provide such datawhile the vehicle 104 is in motion to implement the modification whilethe vehicle 104 is in motion. In some implementations, the vehiclecomputing system 102 can provide the data indicative of the modificationat a current point in time to immediately implement the modification(e.g., for the vehicle's current situation). In some implementations,the vehicle computing system 102 can provide the data indicative of themodification to the one or more systems at a future point in time (e.g.,approaching a future stop, flat travel way, low crowd density area,etc.). For example, the vehicle computing system 102 can determine amodification while the vehicle 104 is in a first geographic area andimplement the modification when the vehicle 104 is in a secondgeographic area.

In some implementations, a future modification of the systems onboardthe vehicle 104 can be based on the current parameters of the vehicle104. For instance, the vehicle computing system 102 can determine thatthe vehicle 104 will enter into an area with low crowd density, lowtraffic, flat terrain, etc. at some future point in time that will allowthe operating characteristics of the sensor system 111 to be modified(e.g., to reduce heat generation). Accordingly, the vehicle computingsystem 102 can determine that the sensor system 112 may currentlyoperate in a manner that generates a higher amount of heat based on thedetermination that the operating characteristic(s) of the sensor system111 can be modified in the future to reduce heat generation. In anotherexample, the vehicle computing system 102 can determine that the vehicle104 is currently travelling in an area with low crowd density, lowtraffic, flat terrain, etc. that allows the operating characteristics ofthe sensor system 111 to be modified (e.g., to reduce heat generation),but that the vehicle 104 will be travelling in an crowded, high trafficarea, that may require significant power consumption by the sensorsystem 111 (e.g., increasing heat generation). Accordingly, the vehiclecomputing system 102 can implement a modification to the operatingcharacteristic(s) of the sensor system 111 to reduce heat generation ata current time, as a pre-cursor to offset the increased amount of futureheat generation by the sensor system 111.

In some implementations, the system(s) of the vehicle 104 can beconfigured to operate in a plurality of operating modes that include,for example, a low heat operating mode (e.g., in which the systemgenerates less heat). By way of example, the sensor system 111 can beconfigured to operate in a plurality of sensor operating modes thatinclude a low heat sensor operating mode in which the sensor(s) 112operate at a decreased frame rate, utilize sub-sampling, etc. Toimplement the determined modification, the vehicle computing system 102can cause the sensor system 111 to enter into the low heat sensoroperating mode.

FIG. 5 depicts a flow diagram of an example method 500 of autonomousvehicle thermal management according to example embodiments of thepresent disclosure. One or more portion(s) of the method 500 can beimplemented by one or more computing devices such as, for example, theone or more computing device(s) of the vehicle computing system 102and/or other systems. Each respective portion of the method 500 can beperformed by any (or any combination) of the one or more computingdevices. Moreover, one or more portion(s) of the method 500 can beimplemented as an algorithm on the hardware components of the device(s)described herein (e.g., as in FIGS. 1 and 6), for example, to controlthe heat generation, power consumption, and/or temperature of anautonomous vehicle. FIG. 5 depicts elements performed in a particularorder for purposes of illustration and discussion. Those of ordinaryskill in the art, using the disclosures provided herein, will understandthat the elements of any of the methods discussed herein can be adapted,rearranged, expanded, omitted, combined, and/or modified in various wayswithout deviating from the scope of the present disclosure.

At (502), the method 500 can include obtaining data associated with avehicle. For instance, the vehicle computing system 102 can obtain dataassociated with a vehicle 104. By way of example, the vehicle computingsystem 102 can obtain data indicative of a motion plan 134 of thevehicle 104. The motion plan 134 can be indicative of a future vehicleaction to be performed by the vehicle 104. Additionally, oralternatively, the vehicle computing system 102 can obtain map data 120indicative of a future geographic area 404, 406, 408 in which thevehicle 104 is planning to be located at a future point in time. Asdescribed herein, the vehicle computing system 102 can obtain also, oralternatively, obtain weather data 121, sensor data 118, and/or othertypes of data.

At (504), the method 500 can include identifying one or more vehicleparameters based at least in part on the data associated with thevehicle. For instance, the vehicle computing system 102 can identify oneor more vehicle parameters 140 associated with the vehicle 104 based atleast in part on the data associated with the vehicle 104. For instance,the vehicle computing system 102 can identify the one or more vehicleparameters 140 based at least in part on the future vehicle action to beperformed by the vehicle 104 (e.g., as indicated in the motion plan134). Additionally, or alternatively, the vehicle computing system 102can identify the one or more vehicle parameters 140 based at least inpart on the future geographic area 404, 406, 408 in which the vehicle104 is to be located (e.g., as indicated by the map data 120, motionplan 134, vehicle route, etc.). The vehicle computing system 102 canalso, or alternatively, identify one or more vehicle parameters 140based at least in part on the weather data 121, the sensor data 118,and/or other types of data.

As described herein, the vehicle parameter(s) 140 can be indicative of avariety of information. For example, the one or more vehicle parameters140 can be indicative of at least one of: a speed of the vehicle 104, acondition of a travel way, a type of the travel way, a geometry of atravel way, a crowd density (e.g., associated with a future geographicarea 404, 406, 408), a traffic pattern, (e.g., associated with a futuregeographic area 404, 406, 408), a future vehicle action (and/or theheading, speed, acceleration, etc. associated therewith), or the weathercondition (associated with a future geographic area 404, 406, 408). Insome implementations, the one or more vehicle parameters 140 can beindicative of other types of vehicle parameters.

At (506), the method 500 can include determining a modification to oneor more operating characteristics of an onboard system of the vehiclebased at least in part on the vehicle parameters. The vehicle computingsystem 102 can determine a modification to one or more operatingcharacteristics of one or more systems onboard the vehicle 104 based atleast in part on the one or more vehicle parameters 140. As describedherein, the vehicle computing system 102 can determine the modificationof the one or more operating characteristics based at least in part onthe one or more vehicle parameters 140 and at least one of a rule-basedalgorithm or a model 202 (e.g., a machine-learned model). For instance,the vehicle computing system 102 can access the rule-based algorithmand/or the model 202 from an accessible memory onboard the vehicle 104(and/or from a memory that is remote from the vehicle 104). The vehiclecomputing system 102 can input the vehicle parameters 140 into therule-based algorithm and/or the model 202. The vehicle computing system102 can obtain, as an output from the rule-based algorithm and/or themodel 202, a modification of one or more operating characteristics ofone or more systems onboard the vehicle 104.

In some implementations, the vehicle computing system 102 can determinea modification to one or more operating characteristics of a sensorsystem 111 of the vehicle 104 based at least in part on the one or morevehicle parameters 140. In some implementations, the modification cancause a decrease in a power consumption of the sensor system 111. Forinstance, the modification to the one or more operating characteristicsof the sensor system 111 of the vehicle 104 can include a decrease in aframe rate of a sensor 112 of the sensor system 111. Additionally, oralternatively, the modification to the one or more operatingcharacteristics of the sensor system 111 of the vehicle 104 can includesub-sampling sensor data 118 obtained via the sensor system 111. Themodification can include a reduction in the data collection and/orprocessing of the collection of data (e.g., by providing some dataduring processing). By way of example, the vehicle computing system candetermine one or more vehicle parameters such as a current and/or futurespeed of the vehicle 104. The vehicle computing system 102 can determinea modification to the sensor system 111 based on the vehicle parameters.The modification can include a decrease (and/or increase) in a spinrate, emission frequency, frame rate, sub-sampling, etc. of the sensorsystem. The vehicle computing system 102 can implement the modificationas the vehicle 104 is travelling (e.g., a decrease/increase in spinrate, emission frequency, frame rate, sub-sampling, etc. in proportionto the vehicle speed decrease/increase).

In some implementations, the operating characteristic(s) of anothersystem of the vehicle 104 can be modified. For instance, the vehiclecomputing system 102 can determine a modification to one or moreoperating characteristics of a motion planning system 128 of the vehicle104 based at least in part on the one or more vehicle parameters 140.The modification the one or more operating characteristics of the motionplanning system 128 can include, for example, a restriction on a speedof the vehicle 104.

At (508), the method 500 can include controlling the temperature of thevehicle. The vehicle computing system 102 can control a heat generationof at least a portion of the vehicle 104 (e.g., the respective systemand the portions of the vehicle 104 affected by the respective system'sheat generation) via implementation of the modification of the one ormore operating characteristics of the one or more systems onboard thevehicle 104. By way of example, the vehicle computing system 102 cancontrol a heat generation of at least a portion of the vehicle 104 viaimplementation of the modification of the one or more operatingcharacteristics of the sensor system 111 of the vehicle 104. This canhelp control the temperature of the sensor system 111 as well as theportions of the vehicle 104, if any, that are affected by the heatgenerated by the sensor system 111. Additionally, or alternatively, thevehicle computing system 102 can control the heat generation of at leastthe portion of the vehicle 104 via the implementation of themodification to the one or more operating characteristics of the motionplanning system 128 of the vehicle, as described herein.

In some implementations, the modification to the operatingcharacteristic(s) of a system onboard the vehicle 104 can include therespective system adjusting from a first mode to a second mode in whichthe system is configured to generate less heat (e.g., due to less powerconsumption). For example, the sensor system 111 can be configured tooperate in a plurality of sensor operating modes. The plurality ofsensor operating modes can include a low heat sensor operating mode inwhich the sensor system 111 generates less heat (e.g., than in one ormore other operating modes). While in the low heat sensor operatingmode, the sensor system 111 can implement one or more of themodifications (e.g., frame-rate reduction, resolution reduction,sub-sampling, etc.) to reduce the power consumption and heat generationof the sensor system. The vehicle computing system 102 can control thetemperature of at least a portion of the vehicle 104 by causing thesensor system 111 to enter into the low heat sensor operating mode.

FIG. 6 depicts example system components of an example system 600according to example embodiments of the present disclosure. The examplesystem 600 can include the vehicle computing system 102, the operationscomputing system 106, and a machine learning computing system 630 thatare communicatively coupled over one or more network(s) 680.

The vehicle computing system 102 can include one or more computingdevice(s) 601. The computing device(s) 601 of the vehicle computingsystem 102 can include processor(s) 602 and a memory 604 (e.g., onboardthe vehicle 104). The one or more processors 602 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 604can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flashmemory devices, etc., and combinations thereof.

The memory 604 can store information that can be accessed by the one ormore processors 602. For instance, the memory 604 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) caninclude computer-readable instructions 606 that can be executed by theone or more processors 602. The instructions 606 can be software writtenin any suitable programming language or can be implemented in hardware.Additionally, or alternatively, the instructions 606 can be executed inlogically and/or virtually separate threads on processor(s) 602.

For example, the memory 604 can store instructions 606 that whenexecuted by the one or more processors 602 cause the one or moreprocessors 602 (the computing system 102) to perform operations such asany of the operations and functions of the vehicle computing system 102,the vehicle 104, or for which the vehicle computing system 102 and/orthe vehicle 104 are configured, as described herein, the operations forpower consumption and thermal management of an autonomous vehicle (e.g.,one or more portions of method 500), and/or any other functions for thevehicle computing system 102, as described herein.

The memory 604 can store data 608 that can be obtained, received,accessed, written, manipulated, created, and/or stored. The data 608 caninclude, for instance, sensor data, state data, predicted data, motionplanning data, data indicative of rule-based algorithms (e.g., fordetermining modifications), data indicative of machine-learned models(e.g., for determining modifications), map data, weather data, otherdata associated with geographic area(s), data indicative of vehicleparameter(s), data indicative of operating characteristic(s) of thesystem(s) onboard the vehicle, data indicative of modification(s) of theoperating characteristic(s), input data, data indicative ofmachine-learned model(s) (e.g., for determining modifications), dataindicative of model outputs, and/or other data/information describedherein. In some implementations, the computing device(s) 601 can obtaindata from one or more memory device(s) that are remote from the vehicle104.

The computing device(s) 601 can also include a communication interface609 used to communicate with one or more other system(s) on-board thevehicle 104 and/or a remote computing device that is remote from thevehicle 104 (e.g., the other systems of FIG. 6, etc.). The communicationinterface 609 can include any circuits, components, software, etc. forcommunicating via one or more networks (e.g., 680). In someimplementations, the communication interface 609 can include, forexample, one or more of a communications controller, receiver,transceiver, transmitter, port, conductors, software and/or hardware forcommunicating data/information.

The operations computing system 106 can perform the operations andfunctions for managing vehicles (e.g., a fleet of autonomous vehicles)and/or otherwise described herein. The operations computing system 106can be located remotely from the vehicle 104. For example, theoperations computing system 106 can operate offline, off-board, etc. Theoperations computing system 106 can include one or more distinctphysical computing devices.

The operations computing system 106 can include one or more computingdevices 620. The one or more computing devices 620 can include one ormore processors 622 and a memory 624. The one or more processors 622 canbe any suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 624 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and combinations thereof.

The memory 624 can store information that can be accessed by the one ormore processors 622. For instance, the memory 624 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 626 that can be obtained, received, accessed, written,manipulated, created, and/or stored. The data 626 can include, forinstance, data indicative of model(s), map data, weather data, dataassociated with geographic area(s), and/or other data or informationdescribed herein. In some implementations, the operations computingsystem 106 can obtain data from one or more memory device(s) that areremote from the operations computing system 106.

The memory 624 can also store computer-readable instructions 628 thatcan be executed by the one or more processors 622. The instructions 628can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 628 can be executed in logically and/or virtually separatethreads on processor(s) 622. For example, the memory 624 can storeinstructions 628 that when executed by the one or more processors 622cause the one or more processors 622 to perform any of the operationsand/or functions of the operations computing system 106 and/or otheroperations and functions.

The computing device(s) 620 can also include a communication interface629 used to communicate with one or more other system(s). Thecommunication interface 629 can include any circuits, components,software, etc. for communicating via one or more networks (e.g., 680).In some implementations, the communication interface 629 can include forexample, one or more of a communications controller, receiver,transceiver, transmitter, port, conductors, software and/or hardware forcommunicating data/information.

According to an aspect of the present disclosure, the vehicle computingsystem 102 and/or the operations computing system 106 can store orinclude one or more machine-learned models 640. As examples, themachine-learned models 640 can be or can otherwise include variousmachine-learned models such as, for example, neural networks (e.g., deepneural networks), support vector machines, decision trees, ensemblemodels, k-nearest neighbors models, Bayesian networks, or other types ofmodels including linear models and/or non-linear models. Example neuralnetworks include feed-forward neural networks, recurrent neural networks(e.g., long short-term memory recurrent neural networks), or other formsof neural networks. The machine-learned models 640 can include the model202, as described herein.

In some implementations, the vehicle computing system 102 and/or theoperations computing system 106 can receive the one or moremachine-learned models 640 from the machine learning computing system630 over the network(s) 680 and can store the one or moremachine-learned models 640 in the memory of the respective system. Thevehicle computing system 102 and/or the operations computing system 106can use or otherwise implement the one or more machine-learned models640 (e.g., by processor(s) 602, 622). In particular, the vehiclecomputing system 102 and/or the operations computing system 106 canimplement the machine learned model(s) 640 to determine a modificationto one or more operating characteristics of a system onboard a vehicle,as described herein.

The machine learning computing system 630 can include one or moreprocessors 632 and a memory 634. The one or more processors 632 can beany suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 634 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and combinations thereof.

The memory 634 can store information that can be accessed by the one ormore processors 632. For instance, the memory 634 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 636 that can be obtained, received, accessed, written,manipulated, created, and/or stored. In some implementations, themachine learning computing system 630 can obtain data from one or morememory devices that are remote from the machine learning computingsystem 630.

The memory 634 can also store computer-readable instructions 638 thatcan be executed by the one or more processors 632. The instructions 638can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 638 can be executed in logically and/or virtually separatethreads on processor(s) 632. The memory 634 can store the instructions638 that when executed by the one or more processors 632 cause the oneor more processors 632 to perform operations. The machine learningcomputing system 630 can include a communication system 639, includingdevices and/or functions similar to that described with respect to thevehicle computing system 102 and/or the operations computing system 106.

In some implementations, the machine learning computing system 630 caninclude one or more server computing devices. If the machine learningcomputing system 630 includes multiple server computing devices, suchserver computing devices can operate according to various computingarchitectures, including, for example, sequential computingarchitectures, parallel computing architectures, or some combinationthereof.

In addition or alternatively to the model(s) 640 at the vehiclecomputing system 102 and/or the operations computing system 106, themachine learning computing system 630 can include one or moremachine-learned models 650. As examples, the machine-learned models 650can be or can otherwise include various machine-learned models such as,for example, neural networks (e.g., deep neural networks), supportvector machines, decision trees, ensemble models, k-nearest neighborsmodels, Bayesian networks, or other types of models including linearmodels and/or non-linear models. Example neural networks includefeed-forward neural networks, recurrent neural networks (e.g., longshort-term memory recurrent neural networks, or other forms of neuralnetworks. The machine-learned models 650 can be similar to and/or thesame as the machine-learned models 640.

As an example, the machine learning computing system 630 can communicatewith the vehicle computing system 102 and/or the operations computingsystem 106 according to a client-server relationship. For example, themachine learning computing system 630 can implement the machine-learnedmodels 650 to provide a web service to the vehicle computing system 102and/or the operations computing system 106. For example, the web servicecan provide machine-learned models to an entity associated with anautonomous vehicle; such that the entity can implement themachine-learned model (e.g., to determine modification(s) to operatingparameter(s) of system(s), etc.). Thus, machine-learned models 650 canbe located and used at the vehicle computing system 102 and/or theoperations computing system 106 and/or machine-learned models 650 can belocated and used at the machine learning computing system 630.

In some implementations, the machine learning computing system 630, thevehicle computing system 102, and/or the operations computing system 106can train the machine-learned models 640 and/or 650 through use of amodel trainer 660. The model trainer 660 can train the machine-learnedmodels 640 and/or 650 using one or more training or learning algorithms.One example training technique is backwards propagation of errors. Insome implementations, the model trainer 660 can perform supervisedtraining techniques using a set of labeled training data. In otherimplementations, the model trainer 660 can perform unsupervised trainingtechniques using a set of unlabeled training data. The model trainer 660can perform a number of generalization techniques to improve thegeneralization capability of the models being trained. Generalizationtechniques include weight decays, dropouts, or other techniques.

In particular, the model trainer 660 can train a machine-learned model640 and/or 650 based on a set of training data 662. The training data662 can include, for example, a number of sets of data from previousevents (e.g., previous event logs indicative of power consumption and/orheat generation). The training data 662 can be associated with aprevious event associated with controlling the temperature of thevehicle, controlling system power consumption, etc. and can allow thetraining data 662 to train a model based on real-world events and thedata associated therewith. In some implementations, the training data662 can be taken from the same vehicle as that which utilizes that model640/650. In this way, the models 640/650 can be trained to determineoutputs (e.g., modification(s) of operating characteristic(s)) in amanner that is tailored to that particular vehicle. Additionally, oralternatively, the training data 662 can be taken from one or moredifferent vehicles that that which is utilizing that model 640/650. Themodel trainer 660 can be implemented in hardware, firmware, and/orsoftware controlling one or more processors.

The network(s) 680 can be any type of network or combination of networksthat allows for communication between devices. In some embodiments, thenetwork(s) 680 can include one or more of a local area network, widearea network, the Internet, secure network, cellular network, meshnetwork, peer-to-peer communication link and/or some combination thereofand can include any number of wired or wireless links. Communicationover the network(s) 680 can be accomplished, for instance, via a networkinterface using any type of protocol, protection scheme, encoding,format, packaging, etc.

FIG. 6 illustrates one example system 600 that can be used to implementthe present disclosure. Other computing systems can be used as well. Forexample, in some implementations, the vehicle computing system 102and/or the operations computing system 106 can include the model trainer660 and the training dataset 662. In such implementations, themachine-learned models 640 can be both trained and used locally at thevehicle computing system 102 and/or the operations computing system 106.As another example, in some implementations, the vehicle computingsystem 102 and/or the operations computing system 106 may not beconnected to other computing systems.

Computing tasks discussed herein as being performed at computingdevice(s) remote from the vehicle can instead be performed at thevehicle (e.g., via the vehicle computing system), or vice versa. Suchconfigurations can be implemented without deviating from the scope ofthe present disclosure. The use of computer-based systems allows for agreat variety of possible configurations, combinations, and divisions oftasks and functionality between and among components.Computer-implemented operations can be performed on a single componentor across multiple components. Computer-implemented tasks and/oroperations can be performed sequentially or in parallel. Data andinstructions can be stored in a single memory device or across multiplememory devices.

While the present subject matter has been described in detail withrespect to specific example embodiments and methods thereof, it will beappreciated that those skilled in the art, upon attaining anunderstanding of the foregoing can readily produce alterations to,variations of, and equivalents to such embodiments. Accordingly, thescope of the present disclosure is by way of example rather than by wayof limitation, and the subject disclosure does not preclude inclusion ofsuch modifications, variations and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.

What is claimed is:
 1. A computer-implemented method of autonomousvehicle thermal management, comprising: identifying, by a computingsystem comprising one or more computing devices, one or more vehicleparameters associated with an autonomous vehicle; determining, by thecomputing system, a modification to one or more operatingcharacteristics of a sensor system of the autonomous vehicle based atleast in part on the one or more vehicle parameters; and controlling, bythe computing system, a heat generation of the sensor system of theautonomous vehicle via implementation of the modification of the one ormore operating characteristics of the sensor system.
 2. Thecomputer-implemented method of claim 1, further comprising: obtaining,by the computing system, data indicative of a motion plan of theautonomous vehicle, wherein the motion plan is indicative of a futurevehicle action to be performed by the autonomous vehicle, and whereinidentifying the one or more vehicle parameters associated with theautonomous vehicle comprises identifying the one or more vehicleparameters based at least in part on the future vehicle action to beperformed by the autonomous vehicle.
 3. The computer-implemented methodof claim 1, wherein the sensor system is configured to operate in aplurality of sensor operating modes, wherein the plurality of sensoroperating modes comprises a low heat sensor operating mode, and whereincontrolling the temperature of at least the portion of the autonomousvehicle comprises: causing, by the computing system, the sensor systemto enter into to the low heat sensor operating mode.
 4. Thecomputer-implemented method of claim 1, further comprising: obtaining,by the computing system, map data indicative of a future geographic areain which the autonomous vehicle is to be located at a future point intime, and wherein identifying the one or more vehicle parametersassociated with the autonomous vehicle comprises identifying the one ormore vehicle parameters based at least in part on the future geographicarea in which the autonomous vehicle is to be located.
 5. Thecomputer-implemented method of claim 4, wherein the one or more vehicleparameters are indicative of a crowd density associated with the futuregeographic area.
 6. The computer-implemented method of claim 4, whereinthe one or more vehicle parameters are indicative of a traffic patternassociated with the future geographic area.
 7. The computer-implementedmethod of claim 1, wherein the modification to the one or more operatingcharacteristics of the sensor system of the autonomous vehicle comprisesa decrease in a frame rate of a sensor of the sensor system.
 8. Thecomputer-implemented method of claim 1, wherein the modification to theone or more operating characteristics of the sensor system of theautonomous vehicle comprises sub-sampling sensor data obtained via thesensor system.
 9. The computer-implemented method of claim 1, furthercomprising: determining, by the computing system, a modification to oneor more operating characteristics of a motion planning system of theautonomous vehicle based at least in part on the one or more vehicleparameters; and controlling, by the computing system, a heat generationof the motion planning system of the autonomous vehicle via theimplementation of the modification to the one or more operatingcharacteristics of the motion planning system.
 10. Thecomputer-implemented method of claim 9, wherein the modification to theone or more operating characteristics of the motion planning systemcomprises a restriction on a speed of the autonomous vehicle.
 11. Acomputing system for autonomous vehicle thermal management, comprising:one or more processors; and one or more tangible, non-transitory,computer readable media that collectively store instructions that whenexecuted by the one or more processors cause the computing system toperform operations, the operations comprising: obtaining data associatedwith an autonomous vehicle, wherein the data is indicative of at leastone of a future action to be performed by the autonomous vehicle, afuture geographic area in which the autonomous vehicle is to be located,or a weather condition to be experienced by the autonomous vehicle;identifying one or more vehicle parameters associated with theautonomous vehicle based at least in part on the data associated withthe autonomous vehicle; determining a modification to one or moreoperating characteristics of one or more systems onboard the autonomousvehicle based at least in part on the one or more vehicle parameters;and controlling a heat generation of at least a portion of theautonomous vehicle via implementation of the modification of the one ormore operating characteristics of the one or more systems onboard theautonomous vehicle.
 12. The computing system of claim 11, wherein theone or more systems onboard the autonomous vehicle comprise at least oneof a sensor system of the autonomous vehicle or a motion planning systemof the autonomous vehicle.
 13. The computing system of claim 12, whereinthe modification of the one or more operating characteristics comprisesadjusting a window of interest of a sensor of the sensor system.
 14. Thecomputing system of claim 12, wherein the modification of the one ormore operating characteristics comprises adjusting cost data utilized bythe motion planning system when planning a motion of the autonomousvehicle.
 15. The computing system of claim 11, wherein the one or morevehicle parameters are indicative of at least one of: a speed of theautonomous vehicle, a condition of a travel way, a type of the travelway, a geometry of a travel way, other parameters indicated by map data,a crowd density, a traffic pattern, the future vehicle action, atemperature of a vehicle computing device, or the weather condition. 16.The computing system of claim 11, wherein the modification of the one ormore operating characteristics of the one or more systems onboard theautonomous vehicle is determined at a first point in time, and whereincontrolling the heat generation of at least the portion of theautonomous vehicle via the implementation of the modification of the oneor more operating characteristics of the one or more systems onboard theautonomous vehicle comprises: providing at a second point in time, bythe computing system, data indicative of the modification to the one ormore systems.
 17. An autonomous vehicle comprising: one or moreprocessors; and one or more tangible, non-transitory, computer readablemedia that collectively store instructions that when executed by the oneor more processors cause the autonomous vehicle to perform operations,the operations comprising: obtaining data associated with the autonomousvehicle; identifying one or more vehicle parameters associated with anautonomous vehicle based at least in part on the data associated withthe autonomous vehicle; determining a modification to one or moreoperating characteristics of one or more systems onboard the autonomousvehicle based at least in part on the one or more vehicle parameters,wherein the one or more systems comprise at least one of a sensor systemof the autonomous vehicle or a motion planning system of the autonomousvehicle; and controlling a heat generation of at least a portion of theautonomous vehicle via implementation of the modification of the one ormore operating characteristics of the one or more systems onboard theautonomous vehicle.
 18. The autonomous vehicle of claim 17, wherein thedata associated with the autonomous vehicle comprises data indicative ofa motion plan of the autonomous vehicle, map data indicative of a futuregeographic area in which the autonomous vehicle is to be located, orweather data indicative of a weather condition to be experienced by theautonomous vehicle.
 19. The autonomous vehicle of claim 17, wherein thesensor system comprises a sensor; and wherein the modification of theone or more operating characteristics comprises at least one of:adjusting an acquisition of image data by the sensor, adjusting a windowof interest associated with the sensor, sub-sampling image data acquiredvia the sensor, reducing the resolution of the image data acquired viathe sensor, or disabling the sensor.
 20. The autonomous vehicle of claim17, wherein determining the modification to the one or more operatingcharacteristics of the one or more systems onboard the autonomousvehicle based at least in part on the one or more vehicle parameterscomprises: determining the modification of the one or more operatingcharacteristics based at least in part on the one or more vehicleparameters and at least one of a rule-based algorithm or amachine-learned model.